This application is a resubmission of a proposal originally reviewed by Special Emphasis Panel A of the HCTDS study section. The current application proposes to investigate the potential benefits of regionalization of certain high-risk procedures. Methodologically, the investigators propose to develop estimates of surgeon- and hospital-volume/outcome relationships by procedure, and then estimate how making use of that relationship by altering the patterns of care from low-volume (high-risk) to high volume (lower risk) hospitals will effect overall mortality. The work is currently proposed in two aims: a third aim contained in the original proposal has been dropped from the resubmission. Use Medicare databases to estimate the relations between hospital procedure volume and outcome (mortality and readmission) using several other covariates to adjust for risk. (modified Charlson index, age, gender, race, and census tract level socio-economic variables) Aim 1 is to predict the impact of two types of regionalization policies: minimum procedure volume standards, and designation of "centers-of-excellence" For Aim 1, the investigators will use a 100% sample of Medicare discharges from US hospitals in patients who had any of 10 specified high risk procedures. They will link those records to any hospitalization in the prior 6 months to acquire a larger set of diagnoses for comorbidities for the purpose of risk adjustment. These patient-level files will be merged with hospital-level and census tract level data (for socioeconomic data). Logistic regression equations predicting mortality as a function of hospital volume (adjusting for the covariates noted) will be estimated. Volume-outcome curves (both adjusted and unadjusted) will be plotted and examined for natural inflection points and cut-off. In Aim 2, the investigators will use the estimates from aim 1 to determine the number of patients who would be translocated to higher volume centers under minimum volume standards vs "centers of excellence" approaches to regionalization. The investigators will then estimate the change in mortality, by assuming that the patient will acquire the average predicted mortality for a similar patient at the larger volume hospital.